1. Field of the Invention
The present invention relates generally to systems and methods for information sharing and knowledge management, and more particularly for anonymously sharing and scoring information pointers, within a system for harvesting community knowledge.
2. Discussion of Background Art
Satisfying information needs in a diverse, heterogeneous information environment is challenging. This is further complicated in that the total amount of information present in the world today likely exceeds the collective memory of all of mankind. As a result, it's virtually impossible to stay up to date on one's current areas of responsibility without the use of advanced recommendation tools.
In order to even approach the process of finding the right information resources or answers to questions, individuals typically must know either where to look, or whom to ask. Enterprise's employees also need to share information and learn from each other. And there needs to be ways of spreading interesting information between them.
This is a challenging task, especially in large enterprises where many of the members are unaware of each other's skill sets, and of all the information resources available to them. There is a lot of knowledge that one's colleagues possess, the sharing of which would be beneficial to all, however the participation costs of sharing such information tend to be high due to the time spent advertising one's knowledge and the resultant privacy lost by doing so. As a result, oftentimes information is not shared within an enterprise, when it otherwise could have been.
Such challenges become ever more significant, as modern enterprises realize that their value and strength as ongoing ventures depends increasingly upon an ability of their members to easily share information. For example, individual members of an enterprise may need to come up to speed on particular areas of knowledge before beginning their new assignment, or the enterprise itself may need to access its current strengths and weaknesses in various product, services, and research areas. Unfortunately however, meeting these information needs is often an elusive goal for many enterprises.
Current systems for collecting information and organizational expertise include Knowledge Databases (K-bases), such as document repositories and corporate directories, and Knowledge Management systems, which rely on users to explicitly describe their personal information, knowledge, and expertise to a centralized K-base.
FIG. 1 is a dataflow diagram of a conventional knowledge management system 100. In a typical architecture, information providing users 102 explicitly decide what descriptive information they provide to a central database 104. An information seeking user 106 then performs a query on the central database 104 in order to find an information provider who perhaps may be able to answer the seeker's question.
There are several significant problems with such systems. Knowledge management systems, like that shown in FIG. 1, require that information providers spend a significant amount of time and effort entering and updating information on the central database 104. For this reasons alone, such systems tend to have very low participation rates. In addition, even information providers, who take time to enter and update such information, may accidentally or purposefully misrepresent their personal information, levels of knowledge, and expertise. Furthermore, they may neglect or be unable to reveal much of their tacit knowledge. Tacit knowledge is commonly known as knowledge a user possesses, but which the user considers trivial, or may not even be consciously aware of.
Because of the inaccuracy and/or incompleteness of such information, it can be difficult for information seekers to come up to speed in an area of technology or for enterprises to access their current strengths and weaknesses
Also, current recommendation engines require that a user's personal information be stored on centralized servers, before data mining and collaborative filtering algorithms can be run in order to provide the user with information source recommendations. Such systems claim not to store any identifiable information about the user, however the user's personal data does leave their local machines resulting in a loss of privacy and anonymity.
Other recommendation engines, such as Firefly (owned by Microsoft Corp.), DirectHit (owned by AskJeeves), and those operating at www.amazon.com are purely server based, have no anonymity, and are limited to recommendations about pointers that are specific to their service.
In response to the concerns discussed above, what is needed is a system and method for reducing the participation costs of information sharing and enhancing the proliferation of knowledge within a company that overcomes the problems of the prior art.